Overview

Dataset statistics

Number of variables27
Number of observations160
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory33.9 KiB
Average record size in memory216.8 B

Variable types

Categorical11
Numeric16

Alerts

Timestamp has a high cardinality: 159 distinct valuesHigh cardinality
Enter your Name: has a high cardinality: 160 distinct valuesHigh cardinality
Add a Screen Shot of your Screen time: has a high cardinality: 160 distinct valuesHigh cardinality
How many social media platforms are you on? has a high cardinality: 64 distinct valuesHigh cardinality
Email Address has a high cardinality: 160 distinct valuesHigh cardinality
Roll Number: is highly overall correlated with Choose Current year:High correlation
Enter Marks(percentage) For 10th Grade: is highly overall correlated with Department:High correlation
Time spend on Facebook? (In hours) is highly overall correlated with How many social media platforms are you on?High correlation
Time spend on Pinterest? (In hours) is highly overall correlated with Time spend on Reddit? (In hours) and 1 other fieldsHigh correlation
Time spend on Twitter? (In hours) is highly overall correlated with How many social media platforms are you on? and 1 other fieldsHigh correlation
Overall time spent on other application? (In hours) is highly overall correlated with How many social media platforms are you on?High correlation
Department: is highly overall correlated with Enter Marks(percentage) For 10th Grade: and 1 other fieldsHigh correlation
Choose Current year: is highly overall correlated with Roll Number: and 1 other fieldsHigh correlation
How many social media platforms are you on? is highly overall correlated with Time spend on Facebook? (In hours) and 4 other fieldsHigh correlation
Time spend on Reddit? (In hours) is highly overall correlated with Time spend on Pinterest? (In hours) and 2 other fieldsHigh correlation
Time spend on Discord? (In hours) is highly overall correlated with Time spend on Pinterest? (In hours) and 1 other fieldsHigh correlation
Time spend on Reddit? (In hours) is highly imbalanced (87.8%)Imbalance
Time spend on Discord? (In hours) is highly imbalanced (74.4%)Imbalance
Timestamp is uniformly distributedUniform
Enter your Name: is uniformly distributedUniform
Add a Screen Shot of your Screen time: is uniformly distributedUniform
Email Address is uniformly distributedUniform
Enter your Name: has unique valuesUnique
Add a Screen Shot of your Screen time: has unique valuesUnique
Email Address has unique valuesUnique
How often do you use social media ? (In hours) has 5 (3.1%) zerosZeros
Time spent on WhatsApp? (In hours) has 3 (1.9%) zerosZeros
Time spend on Instagram? (In hours) has 37 (23.1%) zerosZeros
Time spend on Facebook? (In hours) has 147 (91.9%) zerosZeros
Time spend on YouTube? (In hours) has 12 (7.5%) zerosZeros
Time spend on Snapchat? (In hours) has 62 (38.8%) zerosZeros
Time spend on Pinterest? (In hours) has 134 (83.8%) zerosZeros
Time spend on Twitter? (In hours) has 136 (85.0%) zerosZeros
Time spend on Telegram? (In hours) has 131 (81.9%) zerosZeros
Overall time spent on other application? (In hours) has 34 (21.2%) zerosZeros
What is the maximum time that you have spent away from your phone? (In hours) has 3 (1.9%) zerosZeros
Entertainment usage time while using phone(per day) (In hours) has 4 (2.5%) zerosZeros
Productivity and finance time while using phone (per day)(In hours) has 22 (13.8%) zerosZeros

Reproduction

Analysis started2023-04-13 09:33:39.373969
Analysis finished2023-04-13 09:37:10.382444
Duration3 minutes and 31.01 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Timestamp
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct159
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
4/5/2023 12:16:04
 
2
4/5/2023 11:06:22
 
1
4/6/2023 13:55:49
 
1
4/6/2023 13:16:10
 
1
4/6/2023 13:16:12
 
1
Other values (154)
154 

Length

Max length17
Median length17
Mean length16.99375
Min length16

Characters and Unicode

Total characters2719
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)98.8%

Sample

1st row4/5/2023 11:06:22
2nd row4/5/2023 11:23:32
3rd row4/5/2023 11:47:12
4th row4/5/2023 11:47:52
5th row4/5/2023 11:55:21

Common Values

ValueCountFrequency (%)
4/5/2023 12:16:04 2
 
1.2%
4/5/2023 11:06:22 1
 
0.6%
4/6/2023 13:55:49 1
 
0.6%
4/6/2023 13:16:10 1
 
0.6%
4/6/2023 13:16:12 1
 
0.6%
4/6/2023 13:16:25 1
 
0.6%
4/6/2023 13:17:51 1
 
0.6%
4/6/2023 13:19:35 1
 
0.6%
4/6/2023 13:20:23 1
 
0.6%
4/6/2023 13:21:46 1
 
0.6%
Other values (149) 149
93.1%

Length

2023-04-13T15:07:10.813204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4/5/2023 82
25.6%
4/6/2023 78
24.4%
12:16:04 2
 
0.6%
14:03:59 1
 
0.3%
12:01:23 1
 
0.3%
19:57:53 1
 
0.3%
12:03:21 1
 
0.3%
11:47:12 1
 
0.3%
11:47:52 1
 
0.3%
11:55:21 1
 
0.3%
Other values (151) 151
47.2%

Most occurring characters

ValueCountFrequency (%)
2 484
17.8%
/ 320
11.8%
: 320
11.8%
1 282
10.4%
4 273
10.0%
3 273
10.0%
0 243
8.9%
160
 
5.9%
5 158
 
5.8%
6 117
 
4.3%
Other values (3) 89
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1919
70.6%
Other Punctuation 640
 
23.5%
Space Separator 160
 
5.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 484
25.2%
1 282
14.7%
4 273
14.2%
3 273
14.2%
0 243
12.7%
5 158
 
8.2%
6 117
 
6.1%
8 33
 
1.7%
9 28
 
1.5%
7 28
 
1.5%
Other Punctuation
ValueCountFrequency (%)
/ 320
50.0%
: 320
50.0%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 484
17.8%
/ 320
11.8%
: 320
11.8%
1 282
10.4%
4 273
10.0%
3 273
10.0%
0 243
8.9%
160
 
5.9%
5 158
 
5.8%
6 117
 
4.3%
Other values (3) 89
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 484
17.8%
/ 320
11.8%
: 320
11.8%
1 282
10.4%
4 273
10.0%
3 273
10.0%
0 243
8.9%
160
 
5.9%
5 158
 
5.8%
6 117
 
4.3%
Other values (3) 89
 
3.3%

Enter your Name:
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Patel Areeb Aatif
 
1
Safi punjabi
 
1
Rifah mirkar
 
1
Ayan shaikh
 
1
Adnan Shaikh
 
1
Other values (155)
155 

Length

Max length40
Median length31.5
Mean length17.50625
Min length4

Characters and Unicode

Total characters2801
Distinct characters61
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)100.0%

Sample

1st rowPatel Areeb Aatif
2nd rowSafi punjabi
3rd rowShaikh mohd yaseen
4th rowRida shaikh
5th rowAlfiya patel

Common Values

ValueCountFrequency (%)
Patel Areeb Aatif 1
 
0.6%
Safi punjabi 1
 
0.6%
Rifah mirkar 1
 
0.6%
Ayan shaikh 1
 
0.6%
Adnan Shaikh 1
 
0.6%
Kazi adnan vasim 1
 
0.6%
Momin Zeeshan Ahmed Ashfaque 1
 
0.6%
Shaikh Samiya Azgar 1
 
0.6%
Midhat Ansari 1
 
0.6%
Ansari Mohammed Saif 1
 
0.6%
Other values (150) 150
93.8%

Length

2023-04-13T15:07:10.947038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shaikh 40
 
9.5%
ansari 17
 
4.1%
mohammed 14
 
3.3%
khan 13
 
3.1%
abdul 9
 
2.1%
ahmed 8
 
1.9%
mohd 7
 
1.7%
mohammad 5
 
1.2%
mohammed 4
 
1.0%
sayyed 3
 
0.7%
Other values (261) 299
71.4%

Most occurring characters

ValueCountFrequency (%)
a 433
15.5%
334
 
11.9%
h 218
 
7.8%
i 188
 
6.7%
e 119
 
4.2%
n 118
 
4.2%
d 113
 
4.0%
A 107
 
3.8%
m 104
 
3.7%
r 102
 
3.6%
Other values (51) 965
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1979
70.7%
Uppercase Letter 475
 
17.0%
Space Separator 334
 
11.9%
Decimal Number 10
 
0.4%
Other Punctuation 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 433
21.9%
h 218
11.0%
i 188
9.5%
e 119
 
6.0%
n 118
 
6.0%
d 113
 
5.7%
m 104
 
5.3%
r 102
 
5.2%
s 91
 
4.6%
u 69
 
3.5%
Other values (15) 424
21.4%
Uppercase Letter
ValueCountFrequency (%)
A 107
22.5%
S 96
20.2%
M 67
14.1%
H 32
 
6.7%
K 26
 
5.5%
R 22
 
4.6%
N 17
 
3.6%
I 15
 
3.2%
D 13
 
2.7%
U 10
 
2.1%
Other values (15) 70
14.7%
Decimal Number
ValueCountFrequency (%)
9 2
20.0%
6 2
20.0%
8 1
10.0%
1 1
10.0%
3 1
10.0%
2 1
10.0%
5 1
10.0%
7 1
10.0%
Space Separator
ValueCountFrequency (%)
334
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2454
87.6%
Common 347
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 433
17.6%
h 218
 
8.9%
i 188
 
7.7%
e 119
 
4.8%
n 118
 
4.8%
d 113
 
4.6%
A 107
 
4.4%
m 104
 
4.2%
r 102
 
4.2%
S 96
 
3.9%
Other values (40) 856
34.9%
Common
ValueCountFrequency (%)
334
96.3%
9 2
 
0.6%
. 2
 
0.6%
6 2
 
0.6%
8 1
 
0.3%
1 1
 
0.3%
3 1
 
0.3%
2 1
 
0.3%
5 1
 
0.3%
7 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2801
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 433
15.5%
334
 
11.9%
h 218
 
7.8%
i 188
 
6.7%
e 119
 
4.2%
n 118
 
4.2%
d 113
 
4.0%
A 107
 
3.8%
m 104
 
3.7%
r 102
 
3.6%
Other values (51) 965
34.5%

Roll Number:
Real number (ℝ)

Distinct158
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155339.6
Minimum19763
Maximum221258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:11.195782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19763
5-th percentile20415
Q120545.5
median210449
Q3220534.5
95-th percentile221239.05
Maximum221258
Range201495
Interquartile range (IQR)199989

Descriptive statistics

Standard deviation91280.189
Coefficient of variation (CV)0.587617
Kurtosis-1.3514065
Mean155339.6
Median Absolute Deviation (MAD)10754
Skewness-0.80966384
Sum24854336
Variance8.3320729 × 109
MonotonicityNot monotonic
2023-04-13T15:07:11.353830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20415 2
 
1.2%
20430 2
 
1.2%
210414 1
 
0.6%
210403 1
 
0.6%
210453 1
 
0.6%
210457 1
 
0.6%
210404 1
 
0.6%
210450 1
 
0.6%
210463 1
 
0.6%
210405 1
 
0.6%
Other values (148) 148
92.5%
ValueCountFrequency (%)
19763 1
0.6%
20401 1
0.6%
20407 1
0.6%
20409 1
0.6%
20410 1
0.6%
20411 1
0.6%
20414 1
0.6%
20415 2
1.2%
20417 1
0.6%
20418 1
0.6%
ValueCountFrequency (%)
221258 1
0.6%
221257 1
0.6%
221256 1
0.6%
221253 1
0.6%
221251 1
0.6%
221249 1
0.6%
221241 1
0.6%
221240 1
0.6%
221239 1
0.6%
221238 1
0.6%

Department:
Categorical

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Computer
91 
Artificial Intelligence
32 
Electronics
23 
Information Technology
 
7
Electrical
 
4

Length

Max length23
Median length8
Mean length12.13125
Min length8

Characters and Unicode

Total characters1941
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComputer
2nd rowInformation Technology
3rd rowInformation Technology
4th rowInformation Technology
5th rowElectronics

Common Values

ValueCountFrequency (%)
Computer 91
56.9%
Artificial Intelligence 32
 
20.0%
Electronics 23
 
14.4%
Information Technology 7
 
4.4%
Electrical 4
 
2.5%
Mechanical 3
 
1.9%

Length

2023-04-13T15:07:11.492729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T15:07:11.632900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
computer 91
45.7%
artificial 32
 
16.1%
intelligence 32
 
16.1%
electronics 23
 
11.6%
information 7
 
3.5%
technology 7
 
3.5%
electrical 4
 
2.0%
mechanical 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 224
11.5%
t 189
 
9.7%
i 165
 
8.5%
r 157
 
8.1%
o 142
 
7.3%
l 137
 
7.1%
c 131
 
6.7%
n 111
 
5.7%
m 98
 
5.0%
C 91
 
4.7%
Other values (14) 496
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1703
87.7%
Uppercase Letter 199
 
10.3%
Space Separator 39
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 224
13.2%
t 189
11.1%
i 165
9.7%
r 157
9.2%
o 142
8.3%
l 137
8.0%
c 131
7.7%
n 111
6.5%
m 98
 
5.8%
p 91
 
5.3%
Other values (7) 258
15.1%
Uppercase Letter
ValueCountFrequency (%)
C 91
45.7%
I 39
19.6%
A 32
 
16.1%
E 27
 
13.6%
T 7
 
3.5%
M 3
 
1.5%
Space Separator
ValueCountFrequency (%)
39
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1902
98.0%
Common 39
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 224
11.8%
t 189
9.9%
i 165
 
8.7%
r 157
 
8.3%
o 142
 
7.5%
l 137
 
7.2%
c 131
 
6.9%
n 111
 
5.8%
m 98
 
5.2%
C 91
 
4.8%
Other values (13) 457
24.0%
Common
ValueCountFrequency (%)
39
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1941
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 224
11.5%
t 189
 
9.7%
i 165
 
8.5%
r 157
 
8.1%
o 142
 
7.3%
l 137
 
7.1%
c 131
 
6.7%
n 111
 
5.7%
m 98
 
5.0%
C 91
 
4.7%
Other values (14) 496
25.6%
Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
First year
64 
Second year
49 
Third year
47 

Length

Max length11
Median length10
Mean length10.30625
Min length10

Characters and Unicode

Total characters1649
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond year
2nd rowFirst year
3rd rowSecond year
4th rowSecond year
5th rowThird year

Common Values

ValueCountFrequency (%)
First year 64
40.0%
Second year 49
30.6%
Third year 47
29.4%

Length

2023-04-13T15:07:11.747840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T15:07:11.862825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
year 160
50.0%
first 64
 
20.0%
second 49
 
15.3%
third 47
 
14.7%

Most occurring characters

ValueCountFrequency (%)
r 271
16.4%
e 209
12.7%
160
9.7%
y 160
9.7%
a 160
9.7%
i 111
 
6.7%
d 96
 
5.8%
F 64
 
3.9%
s 64
 
3.9%
t 64
 
3.9%
Other values (6) 290
17.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1329
80.6%
Space Separator 160
 
9.7%
Uppercase Letter 160
 
9.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 271
20.4%
e 209
15.7%
y 160
12.0%
a 160
12.0%
i 111
8.4%
d 96
 
7.2%
s 64
 
4.8%
t 64
 
4.8%
c 49
 
3.7%
o 49
 
3.7%
Other values (2) 96
 
7.2%
Uppercase Letter
ValueCountFrequency (%)
F 64
40.0%
S 49
30.6%
T 47
29.4%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1489
90.3%
Common 160
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 271
18.2%
e 209
14.0%
y 160
10.7%
a 160
10.7%
i 111
7.5%
d 96
 
6.4%
F 64
 
4.3%
s 64
 
4.3%
t 64
 
4.3%
S 49
 
3.3%
Other values (5) 241
16.2%
Common
ValueCountFrequency (%)
160
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1649
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 271
16.4%
e 209
12.7%
160
9.7%
y 160
9.7%
a 160
9.7%
i 111
 
6.7%
d 96
 
5.8%
F 64
 
3.9%
s 64
 
3.9%
t 64
 
3.9%
Other values (6) 290
17.6%
Distinct94
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.837813
Minimum49
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:11.976813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile61.9
Q178.55
median85.9
Q389.4
95-th percentile94.543
Maximum98
Range49
Interquartile range (IQR)10.85

Descriptive statistics

Standard deviation9.730919
Coefficient of variation (CV)0.11746953
Kurtosis1.211239
Mean82.837813
Median Absolute Deviation (MAD)5.2
Skewness-1.1858192
Sum13254.05
Variance94.690784
MonotonicityNot monotonic
2023-04-13T15:07:12.112980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 7
 
4.4%
80 5
 
3.1%
89 5
 
3.1%
90.6 5
 
3.1%
85 5
 
3.1%
80.2 4
 
2.5%
87 4
 
2.5%
89.2 3
 
1.9%
92 3
 
1.9%
86.4 3
 
1.9%
Other values (84) 116
72.5%
ValueCountFrequency (%)
49 1
0.6%
52 1
0.6%
56 1
0.6%
57 2
1.2%
59.78 1
0.6%
60 2
1.2%
62 1
0.6%
63.8 1
0.6%
64.6 1
0.6%
65 2
1.2%
ValueCountFrequency (%)
98 1
0.6%
96.6 1
0.6%
95.5 1
0.6%
95.2 2
1.2%
95 2
1.2%
94.6 1
0.6%
94.54 1
0.6%
94 2
1.2%
93.8 2
1.2%
93.4 1
0.6%
Distinct103
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.319875
Minimum19
Maximum92.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:12.245861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile60
Q172.9325
median79.07
Q383.85
95-th percentile89.7365
Maximum92.84
Range73.84
Interquartile range (IQR)10.9175

Descriptive statistics

Standard deviation9.845498
Coefficient of variation (CV)0.12733463
Kurtosis6.9502292
Mean77.319875
Median Absolute Deviation (MAD)5.115
Skewness-1.7571718
Sum12371.18
Variance96.93383
MonotonicityNot monotonic
2023-04-13T15:07:12.375024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 8
 
5.0%
75 6
 
3.8%
86 5
 
3.1%
78 5
 
3.1%
82 5
 
3.1%
60 4
 
2.5%
79 4
 
2.5%
74 3
 
1.9%
70 3
 
1.9%
73 3
 
1.9%
Other values (93) 114
71.2%
ValueCountFrequency (%)
19 1
 
0.6%
52 1
 
0.6%
53 1
 
0.6%
54.87 1
 
0.6%
56.9 1
 
0.6%
57 1
 
0.6%
60 4
2.5%
61.2 1
 
0.6%
61.57 1
 
0.6%
61.6 1
 
0.6%
ValueCountFrequency (%)
92.84 1
 
0.6%
91.89 3
1.9%
91.78 1
 
0.6%
90 2
1.2%
89.86 1
 
0.6%
89.73 1
 
0.6%
89.44 1
 
0.6%
89 3
1.9%
88.89 1
 
0.6%
88.87 1
 
0.6%

Add a Screen Shot of your Screen time:
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
https://drive.google.com/open?id=1rqW8MeJ6sufzPWXXudkNGkfL3EY94Qpk
 
1
https://drive.google.com/open?id=1Gj_0fIUla_PahgiXiufgHE_aIpwTyCSR
 
1
https://drive.google.com/open?id=11DKId-p59doS4sFhNERCzvi-6Jmk31Xr
 
1
https://drive.google.com/open?id=1xZY53lHXQ0Bb1Foq6ajFKl_RnvRc2o7r
 
1
https://drive.google.com/open?id=1UWxGdtNrgZlcbL9QrMtlNaPa4q5u4GE_
 
1
Other values (155)
155 

Length

Max length66
Median length66
Mean length66
Min length66

Characters and Unicode

Total characters10560
Distinct characters69
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)100.0%

Sample

1st rowhttps://drive.google.com/open?id=1rqW8MeJ6sufzPWXXudkNGkfL3EY94Qpk
2nd rowhttps://drive.google.com/open?id=1Gj_0fIUla_PahgiXiufgHE_aIpwTyCSR
3rd rowhttps://drive.google.com/open?id=1HWisWbixa_bZr0RjBEIxks7_26yIKZbK
4th rowhttps://drive.google.com/open?id=1zn3JdjGfGBBarwz-eywYx_ooj5FQDrZp
5th rowhttps://drive.google.com/open?id=1f2imoo4xSAa0JpCzqttgvR_UnefZo0rS

Common Values

ValueCountFrequency (%)
https://drive.google.com/open?id=1rqW8MeJ6sufzPWXXudkNGkfL3EY94Qpk 1
 
0.6%
https://drive.google.com/open?id=1Gj_0fIUla_PahgiXiufgHE_aIpwTyCSR 1
 
0.6%
https://drive.google.com/open?id=11DKId-p59doS4sFhNERCzvi-6Jmk31Xr 1
 
0.6%
https://drive.google.com/open?id=1xZY53lHXQ0Bb1Foq6ajFKl_RnvRc2o7r 1
 
0.6%
https://drive.google.com/open?id=1UWxGdtNrgZlcbL9QrMtlNaPa4q5u4GE_ 1
 
0.6%
https://drive.google.com/open?id=1yqMMCG--csZU7_nGrvBy3C9bFMz6hdm6 1
 
0.6%
https://drive.google.com/open?id=1KrAKwmueZsMr3_je-dslzKj0S4D6CS-_ 1
 
0.6%
https://drive.google.com/open?id=1ogjwstRs1XI9NOon0k-ScA2_VyLF_Nz4 1
 
0.6%
https://drive.google.com/open?id=1N9bFMFQjKZwsMmf_zsoxSVgScp9a1Fjz 1
 
0.6%
https://drive.google.com/open?id=1_ObIXMqHxldM7hzt8cF097c8ZET_3bWC 1
 
0.6%
Other values (150) 150
93.8%

Length

2023-04-13T15:07:12.501688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://drive.google.com/open?id=1rqw8mej6sufzpwxxudkngkfl3ey94qpk 1
 
0.6%
https://drive.google.com/open?id=1gj_0fiula_pahgixiufghe_aipwtycsr 1
 
0.6%
https://drive.google.com/open?id=14qzrd5ecyiczixgvugqbomx4tf63zwmq 1
 
0.6%
https://drive.google.com/open?id=1hwiswbixa_bzr0rjbeixks7_26yikzbk 1
 
0.6%
https://drive.google.com/open?id=1zn3jdjgfgbbarwz-eywyx_ooj5fqdrzp 1
 
0.6%
https://drive.google.com/open?id=1f2imoo4xsaa0jpczqttgvr_unefzo0rs 1
 
0.6%
https://drive.google.com/open?id=1wiqnz1el3p57t_k2cxpvgljgca98ekak 1
 
0.6%
https://drive.google.com/open?id=12fn8zbg8-2yqk3rjmtoci24eypb03kpl 1
 
0.6%
https://drive.google.com/open?id=19argdz9hcoapbbddgwgl4bz9npumvcsk 1
 
0.6%
https://drive.google.com/open?id=1d3dztujg2gsvpaqbopa7cexuxk1zwnzf 1
 
0.6%
Other values (150) 150
93.8%

Most occurring characters

ValueCountFrequency (%)
o 733
 
6.9%
e 558
 
5.3%
/ 480
 
4.5%
i 406
 
3.8%
d 403
 
3.8%
g 398
 
3.8%
t 398
 
3.8%
p 395
 
3.7%
. 320
 
3.0%
r 248
 
2.3%
Other values (59) 6221
58.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6075
57.5%
Uppercase Letter 2062
 
19.5%
Other Punctuation 1120
 
10.6%
Decimal Number 994
 
9.4%
Math Symbol 160
 
1.5%
Connector Punctuation 79
 
0.7%
Dash Punctuation 70
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 733
 
12.1%
e 558
 
9.2%
i 406
 
6.7%
d 403
 
6.6%
g 398
 
6.6%
t 398
 
6.6%
p 395
 
6.5%
r 248
 
4.1%
s 243
 
4.0%
n 240
 
4.0%
Other values (16) 2053
33.8%
Uppercase Letter
ValueCountFrequency (%)
B 93
 
4.5%
D 92
 
4.5%
Q 92
 
4.5%
F 90
 
4.4%
N 90
 
4.4%
Z 90
 
4.4%
H 89
 
4.3%
E 87
 
4.2%
C 86
 
4.2%
M 86
 
4.2%
Other values (16) 1167
56.6%
Decimal Number
ValueCountFrequency (%)
1 237
23.8%
6 97
9.8%
3 96
9.7%
2 91
 
9.2%
9 91
 
9.2%
0 85
 
8.6%
7 83
 
8.4%
4 78
 
7.8%
8 69
 
6.9%
5 67
 
6.7%
Other Punctuation
ValueCountFrequency (%)
/ 480
42.9%
. 320
28.6%
? 160
 
14.3%
: 160
 
14.3%
Math Symbol
ValueCountFrequency (%)
= 160
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8137
77.1%
Common 2423
 
22.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 733
 
9.0%
e 558
 
6.9%
i 406
 
5.0%
d 403
 
5.0%
g 398
 
4.9%
t 398
 
4.9%
p 395
 
4.9%
r 248
 
3.0%
s 243
 
3.0%
n 240
 
2.9%
Other values (42) 4115
50.6%
Common
ValueCountFrequency (%)
/ 480
19.8%
. 320
13.2%
1 237
9.8%
? 160
 
6.6%
= 160
 
6.6%
: 160
 
6.6%
6 97
 
4.0%
3 96
 
4.0%
2 91
 
3.8%
9 91
 
3.8%
Other values (7) 531
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 733
 
6.9%
e 558
 
5.3%
/ 480
 
4.5%
i 406
 
3.8%
d 403
 
3.8%
g 398
 
3.8%
t 398
 
3.8%
p 395
 
3.7%
. 320
 
3.0%
r 248
 
2.3%
Other values (59) 6221
58.9%
Distinct22
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.75425
Minimum0
Maximum24
Zeros5
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:12.597655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.975
Q12
median3
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4911456
Coefficient of variation (CV)0.92991825
Kurtosis10.283666
Mean3.75425
Median Absolute Deviation (MAD)1
Skewness2.8285664
Sum600.68
Variance12.188098
MonotonicityNot monotonic
2023-04-13T15:07:12.703371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 44
27.5%
3 32
20.0%
4 20
12.5%
1 15
 
9.4%
5 10
 
6.2%
6 8
 
5.0%
0 5
 
3.1%
7 4
 
2.5%
10 4
 
2.5%
15 3
 
1.9%
Other values (12) 15
 
9.4%
ValueCountFrequency (%)
0 5
 
3.1%
0.5 3
 
1.9%
1 15
 
9.4%
1.2 1
 
0.6%
1.5 1
 
0.6%
2 44
27.5%
2.5 1
 
0.6%
3 32
20.0%
3.5 1
 
0.6%
4 20
12.5%
ValueCountFrequency (%)
24 1
 
0.6%
18 1
 
0.6%
17 1
 
0.6%
15 3
 
1.9%
12 1
 
0.6%
10 4
2.5%
9 1
 
0.6%
8 2
 
1.2%
7 4
2.5%
6 8
5.0%
Distinct12
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
Educational, Entertainment, Research/Reading, Gaming
35 
Educational, Entertainment, Research/Reading
27 
Entertainment
26 
Educational, Entertainment
25 
Educational
17 
Other values (7)
30 

Length

Max length52
Median length39
Mean length31.1125
Min length6

Characters and Unicode

Total characters4978
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowEducational, Entertainment, Research/Reading, Gaming
2nd rowEducational, Entertainment, Research/Reading, Gaming
3rd rowEducational, Entertainment, Research/Reading
4th rowEntertainment
5th rowEducational, Entertainment

Common Values

ValueCountFrequency (%)
Educational, Entertainment, Research/Reading, Gaming 35
21.9%
Educational, Entertainment, Research/Reading 27
16.9%
Entertainment 26
16.2%
Educational, Entertainment 25
15.6%
Educational 17
10.6%
Educational, Entertainment, Gaming 10
 
6.2%
Entertainment, Research/Reading 6
 
3.8%
Educational, Research/Reading 4
 
2.5%
Gaming 4
 
2.5%
Research/Reading 3
 
1.9%
Other values (2) 3
 
1.9%

Length

2023-04-13T15:07:12.809089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
entertainment 132
34.9%
educational 118
31.2%
research/reading 76
20.1%
gaming 52
 
13.8%

Most occurring characters

ValueCountFrequency (%)
n 642
12.9%
a 572
11.5%
t 514
 
10.3%
e 492
 
9.9%
i 378
 
7.6%
E 250
 
5.0%
218
 
4.4%
, 218
 
4.4%
r 208
 
4.2%
d 194
 
3.9%
Other values (11) 1292
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4012
80.6%
Uppercase Letter 454
 
9.1%
Other Punctuation 294
 
5.9%
Space Separator 218
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 642
16.0%
a 572
14.3%
t 514
12.8%
e 492
12.3%
i 378
9.4%
r 208
 
5.2%
d 194
 
4.8%
c 194
 
4.8%
m 184
 
4.6%
g 128
 
3.2%
Other values (5) 506
12.6%
Uppercase Letter
ValueCountFrequency (%)
E 250
55.1%
R 152
33.5%
G 52
 
11.5%
Other Punctuation
ValueCountFrequency (%)
, 218
74.1%
/ 76
 
25.9%
Space Separator
ValueCountFrequency (%)
218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4466
89.7%
Common 512
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 642
14.4%
a 572
12.8%
t 514
11.5%
e 492
11.0%
i 378
8.5%
E 250
 
5.6%
r 208
 
4.7%
d 194
 
4.3%
c 194
 
4.3%
m 184
 
4.1%
Other values (8) 838
18.8%
Common
ValueCountFrequency (%)
218
42.6%
, 218
42.6%
/ 76
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 642
12.9%
a 572
11.5%
t 514
 
10.3%
e 492
 
9.9%
i 378
 
7.6%
E 250
 
5.0%
218
 
4.4%
, 218
 
4.4%
r 208
 
4.2%
d 194
 
3.9%
Other values (11) 1292
26.0%

How many social media platforms are you on?
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct64
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
WhatsApp, Instagram, YouTube, Snapchat
28 
WhatsApp, YouTube
14 
WhatsApp, Instagram, YouTube
13 
WhatsApp, Instagram, YouTube, Snapchat, Pinterest
WhatsApp, YouTube, Snapchat
 
8
Other values (59)
88 

Length

Max length109
Median length87
Mean length43.8375
Min length7

Characters and Unicode

Total characters7014
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)26.9%

Sample

1st rowWhatsApp, Instagram, YouTube, Snapchat
2nd rowWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Twitter
3rd rowWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Twitter,
4th rowWhatsApp, Instagram, YouTube, Snapchat, Discord, Pinterest
5th rowWhatsApp, YouTube, Snapchat, Pinterest, Twitter

Common Values

ValueCountFrequency (%)
WhatsApp, Instagram, YouTube, Snapchat 28
17.5%
WhatsApp, YouTube 14
 
8.8%
WhatsApp, Instagram, YouTube 13
 
8.1%
WhatsApp, Instagram, YouTube, Snapchat, Pinterest 9
 
5.6%
WhatsApp, YouTube, Snapchat 8
 
5.0%
WhatsApp, Instagram, Facebook, YouTube, Snapchat 5
 
3.1%
WhatsApp, YouTube, Telegram 4
 
2.5%
WhatsApp, Instagram, YouTube, Snapchat, Telegram 4
 
2.5%
WhatsApp, Instagram, YouTube, Snapchat, Twitter 4
 
2.5%
WhatsApp, Instagram, YouTube, Snapchat, Pinterest, Twitter, Telegram 4
 
2.5%
Other values (54) 67
41.9%

Length

2023-04-13T15:07:12.928769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
whatsapp 157
21.2%
youtube 150
20.3%
instagram 123
16.6%
snapchat 112
15.1%
telegram 49
 
6.6%
pinterest 38
 
5.1%
twitter 38
 
5.1%
facebook 29
 
3.9%
discord 28
 
3.8%
reddit 11
 
1.5%
Other values (5) 5
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a 706
 
10.1%
592
 
8.4%
, 591
 
8.4%
t 558
 
8.0%
p 426
 
6.1%
e 403
 
5.7%
s 347
 
4.9%
u 300
 
4.3%
r 277
 
3.9%
n 273
 
3.9%
Other values (24) 2541
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4784
68.2%
Uppercase Letter 1046
 
14.9%
Space Separator 592
 
8.4%
Other Punctuation 592
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 706
14.8%
t 558
11.7%
p 426
8.9%
e 403
8.4%
s 347
 
7.3%
u 300
 
6.3%
r 277
 
5.8%
n 273
 
5.7%
h 271
 
5.7%
o 240
 
5.0%
Other values (10) 983
20.5%
Uppercase Letter
ValueCountFrequency (%)
T 239
22.8%
W 157
15.0%
A 157
15.0%
Y 150
14.3%
I 123
11.8%
S 112
10.7%
P 39
 
3.7%
F 29
 
2.8%
D 28
 
2.7%
R 11
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 591
99.8%
. 1
 
0.2%
Space Separator
ValueCountFrequency (%)
592
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5830
83.1%
Common 1184
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 706
 
12.1%
t 558
 
9.6%
p 426
 
7.3%
e 403
 
6.9%
s 347
 
6.0%
u 300
 
5.1%
r 277
 
4.8%
n 273
 
4.7%
h 271
 
4.6%
o 240
 
4.1%
Other values (21) 2029
34.8%
Common
ValueCountFrequency (%)
592
50.0%
, 591
49.9%
. 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7014
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 706
 
10.1%
592
 
8.4%
, 591
 
8.4%
t 558
 
8.0%
p 426
 
6.1%
e 403
 
5.7%
s 347
 
4.9%
u 300
 
4.3%
r 277
 
3.9%
n 273
 
3.9%
Other values (24) 2541
36.2%
Distinct18
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.601875
Minimum0
Maximum10
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:13.042793image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.295
Q11
median1
Q32
95-th percentile5
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4399608
Coefficient of variation (CV)0.89892206
Kurtosis9.5066622
Mean1.601875
Median Absolute Deviation (MAD)0
Skewness2.738882
Sum256.3
Variance2.073487
MonotonicityNot monotonic
2023-04-13T15:07:13.141536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 86
53.8%
2 31
 
19.4%
3 9
 
5.6%
0.5 8
 
5.0%
6 5
 
3.1%
5 3
 
1.9%
0 3
 
1.9%
0.1 2
 
1.2%
0.05 2
 
1.2%
0.3 2
 
1.2%
Other values (8) 9
 
5.6%
ValueCountFrequency (%)
0 3
 
1.9%
0.05 2
 
1.2%
0.1 2
 
1.2%
0.2 1
 
0.6%
0.3 2
 
1.2%
0.5 8
 
5.0%
0.9 1
 
0.6%
1 86
53.8%
1.3 1
 
0.6%
1.5 1
 
0.6%
ValueCountFrequency (%)
10 1
 
0.6%
7 1
 
0.6%
6 5
 
3.1%
5 3
 
1.9%
4 2
 
1.2%
3 9
 
5.6%
2.5 1
 
0.6%
2 31
19.4%
1.5 1
 
0.6%
1.3 1
 
0.6%
Distinct21
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.67925
Minimum0
Maximum21
Zeros37
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:13.245072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.275
median1
Q32
95-th percentile5.05
Maximum21
Range21
Interquartile range (IQR)1.725

Descriptive statistics

Standard deviation2.3989651
Coefficient of variation (CV)1.4285932
Kurtosis29.850561
Mean1.67925
Median Absolute Deviation (MAD)1
Skewness4.5245704
Sum268.68
Variance5.7550334
MonotonicityNot monotonic
2023-04-13T15:07:13.341763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 50
31.2%
0 37
23.1%
2 26
16.2%
3 12
 
7.5%
4 6
 
3.8%
0.5 6
 
3.8%
5 4
 
2.5%
6 3
 
1.9%
7 3
 
1.9%
1.5 2
 
1.2%
Other values (11) 11
 
6.9%
ValueCountFrequency (%)
0 37
23.1%
0.07 1
 
0.6%
0.1 1
 
0.6%
0.2 1
 
0.6%
0.3 1
 
0.6%
0.4 1
 
0.6%
0.45 1
 
0.6%
0.5 6
 
3.8%
1 50
31.2%
1.15 1
 
0.6%
ValueCountFrequency (%)
21 1
 
0.6%
14 1
 
0.6%
7 3
 
1.9%
6 3
 
1.9%
5 4
 
2.5%
4 6
 
3.8%
3 12
7.5%
2.43 1
 
0.6%
2 26
16.2%
1.58 1
 
0.6%

Time spend on Facebook? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.281875
Minimum0
Maximum21
Zeros147
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:13.433520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8158932
Coefficient of variation (CV)6.4421932
Kurtosis109.82226
Mean0.281875
Median Absolute Deviation (MAD)0
Skewness10.00041
Sum45.1
Variance3.2974682
MonotonicityNot monotonic
2023-04-13T15:07:13.512389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 147
91.9%
1 6
 
3.8%
3 2
 
1.2%
2 2
 
1.2%
21 1
 
0.6%
8 1
 
0.6%
0.1 1
 
0.6%
ValueCountFrequency (%)
0 147
91.9%
0.1 1
 
0.6%
1 6
 
3.8%
2 2
 
1.2%
3 2
 
1.2%
8 1
 
0.6%
21 1
 
0.6%
ValueCountFrequency (%)
21 1
 
0.6%
8 1
 
0.6%
3 2
 
1.2%
2 2
 
1.2%
1 6
 
3.8%
0.1 1
 
0.6%
0 147
91.9%
Distinct15
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.743125
Minimum0
Maximum16
Zeros12
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:13.609340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7798177
Coefficient of variation (CV)1.02105
Kurtosis26.169373
Mean1.743125
Median Absolute Deviation (MAD)0.8
Skewness3.9763807
Sum278.9
Variance3.1677512
MonotonicityNot monotonic
2023-04-13T15:07:13.704237image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 64
40.0%
2 34
21.2%
3 15
 
9.4%
0 12
 
7.5%
0.5 11
 
6.9%
4 7
 
4.4%
5 5
 
3.1%
0.3 3
 
1.9%
1.5 2
 
1.2%
0.1 2
 
1.2%
Other values (5) 5
 
3.1%
ValueCountFrequency (%)
0 12
 
7.5%
0.1 2
 
1.2%
0.3 3
 
1.9%
0.5 11
 
6.9%
1 64
40.0%
1.5 2
 
1.2%
2 34
21.2%
2.3 1
 
0.6%
3 15
 
9.4%
4 7
 
4.4%
ValueCountFrequency (%)
16 1
 
0.6%
8 1
 
0.6%
7 1
 
0.6%
6 1
 
0.6%
5 5
 
3.1%
4 7
 
4.4%
3 15
9.4%
2.3 1
 
0.6%
2 34
21.2%
1.5 2
 
1.2%
Distinct18
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.954875
Minimum0
Maximum16
Zeros62
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:13.810685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.934675
Coefficient of variation (CV)2.0261029
Kurtosis40.286183
Mean0.954875
Median Absolute Deviation (MAD)1
Skewness5.7993424
Sum152.78
Variance3.7429673
MonotonicityNot monotonic
2023-04-13T15:07:14.009591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 62
38.8%
1 58
36.2%
2 9
 
5.6%
3 7
 
4.4%
0.5 7
 
4.4%
0.3 2
 
1.2%
4 2
 
1.2%
0.1 2
 
1.2%
1.5 2
 
1.2%
0.15 1
 
0.6%
Other values (8) 8
 
5.0%
ValueCountFrequency (%)
0 62
38.8%
0.01 1
 
0.6%
0.1 2
 
1.2%
0.15 1
 
0.6%
0.19 1
 
0.6%
0.2 1
 
0.6%
0.3 2
 
1.2%
0.33 1
 
0.6%
0.5 7
 
4.4%
0.6 1
 
0.6%
ValueCountFrequency (%)
16 1
 
0.6%
15 1
 
0.6%
8 1
 
0.6%
4 2
 
1.2%
3 7
 
4.4%
2 9
 
5.6%
1.5 2
 
1.2%
1 58
36.2%
0.6 1
 
0.6%
0.5 7
 
4.4%

Time spend on Reddit? (In hours)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
0
155 
1
 
3
2
 
1
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters160
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 155
96.9%
1 3
 
1.9%
2 1
 
0.6%
4 1
 
0.6%

Length

2023-04-13T15:07:14.114341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T15:07:14.227367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 155
96.9%
1 3
 
1.9%
2 1
 
0.6%
4 1
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 155
96.9%
1 3
 
1.9%
2 1
 
0.6%
4 1
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 155
96.9%
1 3
 
1.9%
2 1
 
0.6%
4 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 155
96.9%
1 3
 
1.9%
2 1
 
0.6%
4 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 155
96.9%
1 3
 
1.9%
2 1
 
0.6%
4 1
 
0.6%

Time spend on Discord? (In hours)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
0
139 
1
 
11
3
 
2
0.5
 
1
2
 
1
Other values (6)
 
6

Length

Max length41
Median length1
Mean length1.28125
Min length1

Characters and Unicode

Total characters205
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)5.0%

Sample

1st row0
2nd row0.5
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 139
86.9%
1 11
 
6.9%
3 2
 
1.2%
0.5 1
 
0.6%
2 1
 
0.6%
P 1
 
0.6%
9 1
 
0.6%
00 1
 
0.6%
4 1
 
0.6%
I don't use this Social media Application 1
 
0.6%

Length

2023-04-13T15:07:14.329804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 139
83.7%
1 11
 
6.6%
3 2
 
1.2%
don't 1
 
0.6%
application 1
 
0.6%
media 1
 
0.6%
social 1
 
0.6%
this 1
 
0.6%
use 1
 
0.6%
4 1
 
0.6%
Other values (7) 7
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 143
69.8%
1 11
 
5.4%
6
 
2.9%
i 5
 
2.4%
3 3
 
1.5%
a 3
 
1.5%
t 3
 
1.5%
o 3
 
1.5%
n 2
 
1.0%
l 2
 
1.0%
Other values (18) 24
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 161
78.5%
Lowercase Letter 31
 
15.1%
Space Separator 6
 
2.9%
Uppercase Letter 4
 
2.0%
Other Punctuation 3
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5
16.1%
a 3
9.7%
t 3
9.7%
o 3
9.7%
n 2
 
6.5%
l 2
 
6.5%
c 2
 
6.5%
e 2
 
6.5%
s 2
 
6.5%
p 2
 
6.5%
Other values (4) 5
16.1%
Decimal Number
ValueCountFrequency (%)
0 143
88.8%
1 11
 
6.8%
3 3
 
1.9%
4 1
 
0.6%
9 1
 
0.6%
2 1
 
0.6%
5 1
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
I 1
25.0%
P 1
25.0%
S 1
25.0%
A 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
66.7%
' 1
33.3%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 170
82.9%
Latin 35
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5
14.3%
a 3
 
8.6%
t 3
 
8.6%
o 3
 
8.6%
n 2
 
5.7%
l 2
 
5.7%
c 2
 
5.7%
e 2
 
5.7%
s 2
 
5.7%
p 2
 
5.7%
Other values (8) 9
25.7%
Common
ValueCountFrequency (%)
0 143
84.1%
1 11
 
6.5%
6
 
3.5%
3 3
 
1.8%
. 2
 
1.2%
4 1
 
0.6%
9 1
 
0.6%
2 1
 
0.6%
5 1
 
0.6%
' 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 205
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 143
69.8%
1 11
 
5.4%
6
 
2.9%
i 5
 
2.4%
3 3
 
1.5%
a 3
 
1.5%
t 3
 
1.5%
o 3
 
1.5%
n 2
 
1.0%
l 2
 
1.0%
Other values (18) 24
 
11.7%

Time spend on Pinterest? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.169375
Minimum0
Maximum4
Zeros134
Zeros (%)83.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:14.430883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum4
Range4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47437438
Coefficient of variation (CV)2.8007344
Kurtosis27.405281
Mean0.169375
Median Absolute Deviation (MAD)0
Skewness4.3667572
Sum27.1
Variance0.22503105
MonotonicityNot monotonic
2023-04-13T15:07:14.523051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 134
83.8%
1 20
 
12.5%
0.2 3
 
1.9%
4 1
 
0.6%
2 1
 
0.6%
0.5 1
 
0.6%
ValueCountFrequency (%)
0 134
83.8%
0.2 3
 
1.9%
0.5 1
 
0.6%
1 20
 
12.5%
2 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
4 1
 
0.6%
2 1
 
0.6%
1 20
 
12.5%
0.5 1
 
0.6%
0.2 3
 
1.9%
0 134
83.8%

Time spend on Twitter? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3485625
Minimum0
Maximum24
Zeros136
Zeros (%)85.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:14.628149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0789732
Coefficient of variation (CV)5.96442
Kurtosis109.40255
Mean0.3485625
Median Absolute Deviation (MAD)0
Skewness10.032882
Sum55.77
Variance4.3221294
MonotonicityNot monotonic
2023-04-13T15:07:14.722136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 136
85.0%
1 13
 
8.1%
0.15 2
 
1.2%
0.5 2
 
1.2%
2 2
 
1.2%
0.27 1
 
0.6%
24 1
 
0.6%
10 1
 
0.6%
3 1
 
0.6%
0.2 1
 
0.6%
ValueCountFrequency (%)
0 136
85.0%
0.15 2
 
1.2%
0.2 1
 
0.6%
0.27 1
 
0.6%
0.5 2
 
1.2%
1 13
 
8.1%
2 2
 
1.2%
3 1
 
0.6%
10 1
 
0.6%
24 1
 
0.6%
ValueCountFrequency (%)
24 1
 
0.6%
10 1
 
0.6%
3 1
 
0.6%
2 2
 
1.2%
1 13
 
8.1%
0.5 2
 
1.2%
0.27 1
 
0.6%
0.2 1
 
0.6%
0.15 2
 
1.2%
0 136
85.0%
Distinct13
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49125
Minimum0
Maximum24
Zeros131
Zeros (%)81.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:14.812365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4477017
Coefficient of variation (CV)4.9825989
Kurtosis63.809161
Mean0.49125
Median Absolute Deviation (MAD)0
Skewness7.6522795
Sum78.6
Variance5.9912437
MonotonicityNot monotonic
2023-04-13T15:07:14.908947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 131
81.9%
1 16
 
10.0%
0.3 2
 
1.2%
0.5 2
 
1.2%
24 1
 
0.6%
15 1
 
0.6%
0.7 1
 
0.6%
12 1
 
0.6%
2 1
 
0.6%
4 1
 
0.6%
Other values (3) 3
 
1.9%
ValueCountFrequency (%)
0 131
81.9%
0.1 1
 
0.6%
0.2 1
 
0.6%
0.3 2
 
1.2%
0.5 2
 
1.2%
0.7 1
 
0.6%
1 16
 
10.0%
2 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
ValueCountFrequency (%)
24 1
 
0.6%
15 1
 
0.6%
12 1
 
0.6%
4 1
 
0.6%
3 1
 
0.6%
2 1
 
0.6%
1 16
10.0%
0.7 1
 
0.6%
0.5 2
 
1.2%
0.3 2
 
1.2%

Overall time spent on other application? (In hours)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.295625
Minimum0
Maximum24
Zeros34
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:15.009716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median1
Q33
95-th percentile8.1
Maximum24
Range24
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation3.3776242
Coefficient of variation (CV)1.471331
Kurtosis19.192472
Mean2.295625
Median Absolute Deviation (MAD)1
Skewness3.799733
Sum367.3
Variance11.408346
MonotonicityNot monotonic
2023-04-13T15:07:15.112644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 39
24.4%
0 34
21.2%
2 27
16.9%
3 12
 
7.5%
4 11
 
6.9%
5 8
 
5.0%
0.5 7
 
4.4%
0.3 4
 
2.5%
10 3
 
1.9%
6 2
 
1.2%
Other values (11) 13
 
8.1%
ValueCountFrequency (%)
0 34
21.2%
0.2 1
 
0.6%
0.3 4
 
2.5%
0.5 7
 
4.4%
1 39
24.4%
1.4 1
 
0.6%
1.5 2
 
1.2%
2 27
16.9%
2.5 1
 
0.6%
3 12
 
7.5%
ValueCountFrequency (%)
24 1
 
0.6%
23 1
 
0.6%
12 2
 
1.2%
11 1
 
0.6%
10 3
 
1.9%
8 1
 
0.6%
7 1
 
0.6%
6 2
 
1.2%
5 8
5.0%
4 11
6.9%
Distinct18
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.844375
Minimum0
Maximum16
Zeros3
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:15.213011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median8
Q314
95-th percentile16
Maximum16
Range16
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.0265552
Coefficient of variation (CV)0.56833357
Kurtosis-1.3110954
Mean8.844375
Median Absolute Deviation (MAD)4
Skewness0.049206675
Sum1415.1
Variance25.266257
MonotonicityNot monotonic
2023-04-13T15:07:15.308737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
16 23
14.4%
10 16
10.0%
15 15
9.4%
4 14
8.8%
2 13
8.1%
12 12
7.5%
5 11
6.9%
8 10
 
6.2%
7 10
 
6.2%
6 9
 
5.6%
Other values (8) 27
16.9%
ValueCountFrequency (%)
0 3
 
1.9%
0.1 1
 
0.6%
1 3
 
1.9%
2 13
8.1%
3 7
4.4%
4 14
8.8%
5 11
6.9%
6 9
5.6%
7 10
6.2%
8 10
6.2%
ValueCountFrequency (%)
16 23
14.4%
15 15
9.4%
14 6
 
3.8%
13 1
 
0.6%
12 12
7.5%
11 1
 
0.6%
10 16
10.0%
9 5
 
3.1%
8 10
6.2%
7 10
6.2%
Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
No
79 
Maybe
62 
Yes
19 

Length

Max length5
Median length3
Mean length3.28125
Min length2

Characters and Unicode

Total characters525
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaybe
2nd rowMaybe
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 79
49.4%
Maybe 62
38.8%
Yes 19
 
11.9%

Length

2023-04-13T15:07:15.419442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-13T15:07:15.539122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
no 79
49.4%
maybe 62
38.8%
yes 19
 
11.9%

Most occurring characters

ValueCountFrequency (%)
e 81
15.4%
N 79
15.0%
o 79
15.0%
M 62
11.8%
a 62
11.8%
y 62
11.8%
b 62
11.8%
Y 19
 
3.6%
s 19
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365
69.5%
Uppercase Letter 160
30.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 81
22.2%
o 79
21.6%
a 62
17.0%
y 62
17.0%
b 62
17.0%
s 19
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
N 79
49.4%
M 62
38.8%
Y 19
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 525
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 81
15.4%
N 79
15.0%
o 79
15.0%
M 62
11.8%
a 62
11.8%
y 62
11.8%
b 62
11.8%
Y 19
 
3.6%
s 19
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 525
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 81
15.4%
N 79
15.0%
o 79
15.0%
M 62
11.8%
a 62
11.8%
y 62
11.8%
b 62
11.8%
Y 19
 
3.6%
s 19
 
3.6%
Distinct17
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.593125
Minimum0
Maximum15
Zeros4
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:15.634406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.975
Q11
median2
Q33
95-th percentile6.1
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2283796
Coefficient of variation (CV)0.85934138
Kurtosis8.0294363
Mean2.593125
Median Absolute Deviation (MAD)1
Skewness2.4384577
Sum414.9
Variance4.9656757
MonotonicityNot monotonic
2023-04-13T15:07:15.741450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 51
31.9%
1 43
26.9%
3 20
 
12.5%
4 13
 
8.1%
5 8
 
5.0%
6 5
 
3.1%
0 4
 
2.5%
8 3
 
1.9%
10 3
 
1.9%
1.3 2
 
1.2%
Other values (7) 8
 
5.0%
ValueCountFrequency (%)
0 4
 
2.5%
0.1 1
 
0.6%
0.2 1
 
0.6%
0.5 2
 
1.2%
1 43
26.9%
1.3 2
 
1.2%
1.5 1
 
0.6%
2 51
31.9%
2.5 1
 
0.6%
3 20
 
12.5%
ValueCountFrequency (%)
15 1
 
0.6%
11 1
 
0.6%
10 3
 
1.9%
8 3
 
1.9%
6 5
 
3.1%
5 8
 
5.0%
4 13
 
8.1%
3 20
 
12.5%
2.5 1
 
0.6%
2 51
31.9%
Distinct15
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.30125
Minimum0
Maximum15
Zeros22
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-04-13T15:07:15.845932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6.2
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4612976
Coefficient of variation (CV)1.0695481
Kurtosis7.9112989
Mean2.30125
Median Absolute Deviation (MAD)1
Skewness2.5011509
Sum368.2
Variance6.0579858
MonotonicityNot monotonic
2023-04-13T15:07:15.939680image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 50
31.2%
2 28
17.5%
3 23
14.4%
0 22
13.8%
4 15
 
9.4%
5 6
 
3.8%
10 6
 
3.8%
0.5 3
 
1.9%
2.5 1
 
0.6%
0.2 1
 
0.6%
Other values (5) 5
 
3.1%
ValueCountFrequency (%)
0 22
13.8%
0.2 1
 
0.6%
0.5 3
 
1.9%
1 50
31.2%
1.5 1
 
0.6%
2 28
17.5%
2.5 1
 
0.6%
3 23
14.4%
3.5 1
 
0.6%
4 15
 
9.4%
ValueCountFrequency (%)
15 1
 
0.6%
13 1
 
0.6%
10 6
 
3.8%
6 1
 
0.6%
5 6
 
3.8%
4 15
9.4%
3.5 1
 
0.6%
3 23
14.4%
2.5 1
 
0.6%
2 28
17.5%

Email Address
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct160
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
areebpatel079@gmail.com
 
1
safipunjabi007@gmail.com
 
1
sarcarzam@gmail.com
 
1
ayanshaikh3096@gmail.com
 
1
as9215600@gmail.com
 
1
Other values (155)
155 

Length

Max length31
Median length28
Mean length23.775
Min length18

Characters and Unicode

Total characters3804
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)100.0%

Sample

1st rowareebpatel079@gmail.com
2nd rowsafipunjabi007@gmail.com
3rd rowshaikhyaseen123838@gmail.com
4th rowridashaikh12786@gmail.com
5th rowpatelalfiya2004@gmail.com

Common Values

ValueCountFrequency (%)
areebpatel079@gmail.com 1
 
0.6%
safipunjabi007@gmail.com 1
 
0.6%
sarcarzam@gmail.com 1
 
0.6%
ayanshaikh3096@gmail.com 1
 
0.6%
as9215600@gmail.com 1
 
0.6%
kaziadnan456@gmail.com 1
 
0.6%
mzeeshan0109@gmail.com 1
 
0.6%
as3932004@gmail.com 1
 
0.6%
ansarimidhat45@gmail.com 1
 
0.6%
iamsaif2005@gmail.com 1
 
0.6%
Other values (150) 150
93.8%

Length

2023-04-13T15:07:16.061462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
areebpatel079@gmail.com 1
 
0.6%
safipunjabi007@gmail.com 1
 
0.6%
zufisha08@gmail.com 1
 
0.6%
shaikhyaseen123838@gmail.com 1
 
0.6%
ridashaikh12786@gmail.com 1
 
0.6%
patelalfiya2004@gmail.com 1
 
0.6%
jmoizahmed@gmail.com 1
 
0.6%
shaikhmohtashim11@gmail.com 1
 
0.6%
muzzammilmohd987@gmail.com 1
 
0.6%
mahinoorshaikh777@gmail.com 1
 
0.6%
Other values (150) 150
93.8%

Most occurring characters

ValueCountFrequency (%)
a 551
14.5%
m 394
 
10.4%
i 315
 
8.3%
l 219
 
5.8%
o 194
 
5.1%
. 184
 
4.8%
c 171
 
4.5%
g 171
 
4.5%
@ 160
 
4.2%
h 160
 
4.2%
Other values (28) 1285
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3027
79.6%
Decimal Number 431
 
11.3%
Other Punctuation 344
 
9.0%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 551
18.2%
m 394
13.0%
i 315
10.4%
l 219
 
7.2%
o 194
 
6.4%
c 171
 
5.6%
g 171
 
5.6%
h 160
 
5.3%
s 147
 
4.9%
r 102
 
3.4%
Other values (15) 603
19.9%
Decimal Number
ValueCountFrequency (%)
0 90
20.9%
1 57
13.2%
2 53
12.3%
5 43
10.0%
7 38
8.8%
4 35
 
8.1%
6 31
 
7.2%
9 29
 
6.7%
8 29
 
6.7%
3 26
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 184
53.5%
@ 160
46.5%
Uppercase Letter
ValueCountFrequency (%)
Z 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3029
79.6%
Common 775
 
20.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 551
18.2%
m 394
13.0%
i 315
10.4%
l 219
 
7.2%
o 194
 
6.4%
c 171
 
5.6%
g 171
 
5.6%
h 160
 
5.3%
s 147
 
4.9%
r 102
 
3.4%
Other values (16) 605
20.0%
Common
ValueCountFrequency (%)
. 184
23.7%
@ 160
20.6%
0 90
11.6%
1 57
 
7.4%
2 53
 
6.8%
5 43
 
5.5%
7 38
 
4.9%
4 35
 
4.5%
6 31
 
4.0%
9 29
 
3.7%
Other values (2) 55
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 551
14.5%
m 394
 
10.4%
i 315
 
8.3%
l 219
 
5.8%
o 194
 
5.1%
. 184
 
4.8%
c 171
 
4.5%
g 171
 
4.5%
@ 160
 
4.2%
h 160
 
4.2%
Other values (28) 1285
33.8%

Interactions

2023-04-13T15:07:06.091435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:01.240214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:19.619406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:35.327875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:43.617871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:52.113187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:01.411312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:12.208672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:24.063220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:37.729523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:51.563807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:08.555441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:25.402401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:34.807052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:44.035272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:54.514990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:06.343395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:01.732254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:19.932571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:35.592855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:43.842273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:52.330606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:01.661731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:12.443462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:24.322773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:37.982735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:51.846750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:08.842424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:25.682653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:35.045148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:44.270952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:54.763327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:06.530511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:02.166094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:20.341265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:35.833115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:44.049262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:52.535712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:01.924249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:12.673332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:24.553181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:38.204454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:52.162773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:09.194066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:25.969112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:35.282634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:44.474609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:54.985452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:06.729979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:02.778641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:20.809577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:36.090051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:44.270230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:52.759178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:02.214900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:12.896479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:24.794526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:38.463901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:52.580047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:09.653026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:26.263820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:35.536488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:44.727829image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:55.249268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:06.920470image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:03.683840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:21.387967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:36.381808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:44.529899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:53.014278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:02.481916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:13.165774image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:25.075167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:38.798462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:53.156575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:10.289073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:26.604256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:35.822343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:45.018052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:55.557696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:07.106446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:04.644525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:22.054387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:36.727297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:44.836784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:53.310908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:02.783763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:13.473093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:25.443183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:39.255575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:53.928825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:11.065685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:27.000133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:36.284775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:45.376925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:55.916421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:07.243556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:05.681682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:22.862970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:37.105102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:45.196472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:53.641338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:03.135917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:13.833808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:25.947969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:39.882934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:54.858757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:11.973444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:27.461602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:36.690339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:45.789113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:56.307690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:07.376995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:06.783983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:23.724375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:37.662969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:45.590258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:54.017140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:03.542180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:14.338881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:26.654726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:40.710053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:55.836974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:12.795868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:28.066803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:37.158053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:46.227120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:56.743048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:07.530165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:07.830306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:24.653946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:38.124593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:46.020688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:54.426517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:04.084780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:15.002214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:27.598132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:41.694872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:56.834106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:13.778111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:28.772595image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:37.664831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:46.725672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:57.214181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:07.696720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:08.999900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:25.733473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:38.622094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:46.482726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:54.882782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:04.755853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:15.862815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:28.833424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:42.743634image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:57.964564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:14.944228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:29.407542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:38.195904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:47.294365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:57.953740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:07.868262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:10.427270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:27.025096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:39.208885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:47.034612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:55.427463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:05.627942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:16.953980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:30.029438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:43.826146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:59.275113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:16.334272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:30.085600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:38.816683image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:47.973015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:58.779361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:08.021486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:12.000892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:28.756627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:39.855077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:47.702210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:56.142230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:06.743144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:18.212675image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:31.252575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:45.036538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:00.894652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:17.845287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:30.857249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:39.530459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:48.815969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:59.898750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:08.172217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:13.779906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:30.188033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:40.592463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:48.511578image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:57.487963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:08.042217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:19.573968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:32.587884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:46.475413image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:02.729566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:19.524972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:31.674049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:40.384997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:49.886463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:01.378207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:08.321568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:15.789292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:31.841405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:41.469475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:49.588009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:58.677406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:09.474887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:21.027841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:34.106997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:48.126523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:04.820590image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:21.362498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:32.635657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:41.466077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:51.311791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:02.989906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:08.465096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:18.010140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:33.883608image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:42.593970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:51.001693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:00.137933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:11.024225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:22.715982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:36.185967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:50.116979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:07.029648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:23.871135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:33.811639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:42.843841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:53.146864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:04.758801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:08.603808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:19.367218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:35.084012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:43.394934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:04:51.914469image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:01.153984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:11.982659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:23.833588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:37.486709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:05:51.339133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:08.323061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:25.154580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:34.590630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:43.811870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:06:54.288325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-13T15:07:05.847806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-13T15:07:16.194351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Roll Number:Enter Marks(percentage) For 10th Grade:Enter Marks (percentage) For Previous Semester:How often do you use social media ? (In hours)Time spent on WhatsApp? (In hours)Time spend on Instagram? (In hours)Time spend on Facebook? (In hours)Time spend on YouTube? (In hours)Time spend on Snapchat? (In hours)Time spend on Pinterest? (In hours)Time spend on Twitter? (In hours)Time spend on Telegram? (In hours)Overall time spent on other application? (In hours)What is the maximum time that you have spent away from your phone? (In hours)Entertainment usage time while using phone(per day) (In hours)Productivity and finance time while using phone (per day)(In hours)Department:Choose Current year:According to you, what need does social media fulfill?How many social media platforms are you on?Time spend on Reddit? (In hours)Time spend on Discord? (In hours)Do you consider yourself to be addicted to social media?
Roll Number:1.000-0.336-0.3050.005-0.213-0.035-0.196-0.082-0.045-0.265-0.195-0.057-0.0800.089-0.013-0.1000.3940.9530.1290.0000.1440.1650.000
Enter Marks(percentage) For 10th Grade:-0.3361.0000.4460.1770.2570.045-0.011-0.002-0.0530.0420.051-0.0830.015-0.2170.0160.1600.5130.4050.0000.0000.0000.0000.000
Enter Marks (percentage) For Previous Semester:-0.3050.4461.0000.1900.129-0.0070.057-0.098-0.0100.1060.094-0.0460.006-0.0900.0900.2020.1840.1390.0000.0000.0000.0000.000
How often do you use social media ? (In hours)0.0050.1770.1901.0000.1540.4410.0570.2240.2600.2280.0470.0020.074-0.1700.4860.1800.0620.0210.1260.0420.0000.2980.247
Time spent on WhatsApp? (In hours)-0.2130.2570.1290.1541.0000.2110.2290.2870.2900.1170.015-0.0200.108-0.1050.1480.1500.0000.2310.0080.3900.1790.0000.080
Time spend on Instagram? (In hours)-0.0350.045-0.0070.4410.2111.0000.2560.1050.4440.3060.1070.0330.024-0.1580.4810.2300.1890.1540.0000.3870.0360.2230.159
Time spend on Facebook? (In hours)-0.196-0.0110.0570.0570.2290.2561.0000.1800.2480.1870.3350.3690.213-0.2210.2180.0690.0000.0760.0000.6670.0000.0000.000
Time spend on YouTube? (In hours)-0.082-0.002-0.0980.2240.2870.1050.1801.0000.1820.0170.1430.1100.2080.0050.2340.2390.0000.1620.0000.3660.3820.2490.175
Time spend on Snapchat? (In hours)-0.045-0.053-0.0100.2600.2900.4440.2480.1821.0000.2420.1990.0650.1240.0120.2990.1440.1240.0460.0000.2440.4040.1440.068
Time spend on Pinterest? (In hours)-0.2650.0420.1060.2280.1170.3060.1870.0170.2421.0000.3390.2380.092-0.0390.1990.1260.0000.2140.0000.4200.6680.6310.101
Time spend on Twitter? (In hours)-0.1950.0510.0940.0470.0150.1070.3350.1430.1990.3391.0000.4320.210-0.0620.1390.1730.0000.0210.0000.5990.5660.3250.178
Time spend on Telegram? (In hours)-0.057-0.083-0.0460.002-0.0200.0330.3690.1100.0650.2380.4321.0000.265-0.0410.0620.0830.1350.0800.0000.4330.3790.1770.000
Overall time spent on other application? (In hours)-0.0800.0150.0060.0740.1080.0240.2130.2080.1240.0920.2100.2651.0000.0570.2170.0040.1050.0600.0930.5120.4090.1660.208
What is the maximum time that you have spent away from your phone? (In hours)0.089-0.217-0.090-0.170-0.105-0.158-0.2210.0050.012-0.039-0.062-0.0410.0571.000-0.1790.1330.0790.0000.0000.0650.0000.0000.104
Entertainment usage time while using phone(per day) (In hours)-0.0130.0160.0900.4860.1480.4810.2180.2340.2990.1990.1390.0620.217-0.1791.0000.2740.0000.0000.0000.3520.1110.0000.355
Productivity and finance time while using phone\n(per day)(In hours)-0.1000.1600.2020.1800.1500.2300.0690.2390.1440.1260.1730.0830.0040.1330.2741.0000.0000.1020.0000.2210.1490.1970.000
Department:0.3940.5130.1840.0620.0000.1890.0000.0000.1240.0000.0000.1350.1050.0790.0000.0001.0000.5120.0910.2510.0000.3010.000
Choose Current year:0.9530.4050.1390.0210.2310.1540.0760.1620.0460.2140.0210.0800.0600.0000.0000.1020.5121.0000.2100.0000.0710.0970.116
According to you, what need does social media fulfill?0.1290.0000.0000.1260.0080.0000.0000.0000.0000.0000.0000.0000.0930.0000.0000.0000.0910.2101.0000.1450.0000.0510.025
How many social media platforms are you on?0.0000.0000.0000.0420.3900.3870.6670.3660.2440.4200.5990.4330.5120.0650.3520.2210.2510.0000.1451.0000.7840.5340.181
Time spend on Reddit? (In hours)0.1440.0000.0000.0000.1790.0360.0000.3820.4040.6680.5660.3790.4090.0000.1110.1490.0000.0710.0000.7841.0000.4720.106
Time spend on Discord? (In hours)0.1650.0000.0000.2980.0000.2230.0000.2490.1440.6310.3250.1770.1660.0000.0000.1970.3010.0970.0510.5340.4721.0000.142
Do you consider yourself to be addicted to social media?0.0000.0000.0000.2470.0800.1590.0000.1750.0680.1010.1780.0000.2080.1040.3550.0000.0000.1160.0250.1810.1060.1421.000

Missing values

2023-04-13T15:07:09.054715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-13T15:07:09.679034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TimestampEnter your Name:Roll Number:Department:Choose Current year:Enter Marks(percentage) For 10th Grade:Enter Marks (percentage) For Previous Semester:Add a Screen Shot of your Screen time:How often do you use social media ? (In hours)According to you, what need does social media fulfill?How many social media platforms are you on?Time spent on WhatsApp? (In hours)Time spend on Instagram? (In hours)Time spend on Facebook? (In hours)Time spend on YouTube? (In hours)Time spend on Snapchat? (In hours)Time spend on Reddit? (In hours)Time spend on Discord? (In hours)Time spend on Pinterest? (In hours)Time spend on Twitter? (In hours)Time spend on Telegram? (In hours)Overall time spent on other application? (In hours)What is the maximum time that you have spent away from your phone? (In hours)Do you consider yourself to be addicted to social media?Entertainment usage time while using phone(per day) (In hours)Productivity and finance time while using phone (per day)(In hours)Email Address
04/5/2023 11:06:22Patel Areeb Aatif210414ComputerSecond year93.2080.63https://drive.google.com/open?id=1rqW8MeJ6sufzPWXXudkNGkfL3EY94Qpk17.0Educational, Entertainment, Research/Reading, GamingWhatsApp, Instagram, YouTube, Snapchat1.02.00.00.01.0000.00.00.02.07.0Maybe2.01.0areebpatel079@gmail.com
14/5/2023 11:23:32Safi punjabi220816Information TechnologyFirst year85.0065.00https://drive.google.com/open?id=1Gj_0fIUla_PahgiXiufgHE_aIpwTyCSR10.0Educational, Entertainment, Research/Reading, GamingWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Twitter1.02.00.03.02.000.50.00.00.01.016.0Maybe4.03.0safipunjabi007@gmail.com
24/5/2023 11:47:12Shaikh mohd yaseen210817Information TechnologySecond year85.0063.00https://drive.google.com/open?id=1HWisWbixa_bZr0RjBEIxks7_26yIKZbK10.0Educational, Entertainment, Research/ReadingWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Twitter,3.07.00.05.01.0020.01.01.02.010.0Yes5.02.0shaikhyaseen123838@gmail.com
34/5/2023 11:47:52Rida shaikh220882Information TechnologySecond year62.0069.00https://drive.google.com/open?id=1zn3JdjGfGBBarwz-eywYx_ooj5FQDrZp2.0EntertainmentWhatsApp, Instagram, YouTube, Snapchat, Discord, Pinterest1.03.00.02.01.0000.00.00.02.09.0No3.05.0ridashaikh12786@gmail.com
44/5/2023 11:55:21Alfiya patel20529ElectronicsThird year77.2081.67https://drive.google.com/open?id=1f2imoo4xSAa0JpCzqttgvR_UnefZo0rS2.0Educational, EntertainmentWhatsApp, YouTube, Snapchat, Pinterest, Twitter1.00.00.03.01.0000.01.00.01.016.0No2.01.0patelalfiya2004@gmail.com
54/5/2023 11:56:44Moiz Ahmed Shabbir Jamedar20516ElectronicsThird year72.0060.00https://drive.google.com/open?id=1WiqNz1eL3p57t_K2cxPvGlJgCA98EkAk1.0Educational, Entertainment, Research/ReadingWhatsApp, Instagram, YouTube, Snapchat, Pinterest1.01.00.00.01.0000.00.00.00.06.0Maybe2.02.0jmoizahmed@gmail.com
64/5/2023 11:57:51Shaikh mohammed mohtashim mukhtar Ahmed20548Artificial IntelligenceThird year60.0060.00https://drive.google.com/open?id=12FN8ZbG8-2YqK3rjmTocI24eyPb03kPl0.0EntertainmentWhatsApp5.00.00.05.01.0000.00.00.011.011.0No11.00.0shaikhmohtashim11@gmail.com
74/5/2023 12:01:23Supariwala Mohd Muzzammil Ashfak20561ElectronicsThird year57.0065.00https://drive.google.com/open?id=19ArGDZ9HcoapbBddgWgL4BZ9NpUMvcSK2.0Educational, Entertainment, GamingWhatsApp, Instagram, Facebook, YouTube, Snapchat, Telegram1.01.00.01.01.0000.00.01.03.010.0Maybe2.01.0muzzammilmohd987@gmail.com
84/5/2023 12:02:26Shaikh Mahinoor Mohammed Yasin20544ElectronicsThird year69.4075.28https://drive.google.com/open?id=1D3dZTUjg2GsvPAqBopa7cexuxK1ZWNzF0.0EducationalWhatsApp, YouTube, Snapchat,2.00.00.01.00.0000.00.00.01.015.0No1.01.0mahinoorshaikh777@gmail.com
94/5/2023 12:03:21Zau-fishan Rangrez Mohammed Aslam20531ElectronicsThird year59.7871.16https://drive.google.com/open?id=14Qzrd5eCyICZixgvUgqBoMX4tf63ZWmq1.0EducationalWhatsApp, YouTube, Snapchat1.01.00.01.01.0000.00.00.04.015.0Maybe1.01.0zufisha08@gmail.com
TimestampEnter your Name:Roll Number:Department:Choose Current year:Enter Marks(percentage) For 10th Grade:Enter Marks (percentage) For Previous Semester:Add a Screen Shot of your Screen time:How often do you use social media ? (In hours)According to you, what need does social media fulfill?How many social media platforms are you on?Time spent on WhatsApp? (In hours)Time spend on Instagram? (In hours)Time spend on Facebook? (In hours)Time spend on YouTube? (In hours)Time spend on Snapchat? (In hours)Time spend on Reddit? (In hours)Time spend on Discord? (In hours)Time spend on Pinterest? (In hours)Time spend on Twitter? (In hours)Time spend on Telegram? (In hours)Overall time spent on other application? (In hours)What is the maximum time that you have spent away from your phone? (In hours)Do you consider yourself to be addicted to social media?Entertainment usage time while using phone(per day) (In hours)Productivity and finance time while using phone (per day)(In hours)Email Address
1504/6/2023 14:30:52Sarah Ansari20409ComputerThird year94.091.89https://drive.google.com/open?id=1hEL2pvDVgkZ4U0tU96bdyOO4Wsm0wUH25.0EntertainmentWhatsApp, Instagram, YouTube, Snapchat, Pinterest3.01.000.01.01.00I don't use this Social media Application1.00.000.02.08.0No2.00.0sarahansari0007@gmail.com
1514/6/2023 14:32:42Iqra20401ComputerThird year88.087.00https://drive.google.com/open?id=1TlKZe1rEk_6Lvq3WvbRnbZs1zqMjPUnM2.0EducationalWhatsApp, Instagram, YouTube, Snapchat2.02.000.02.01.0000.00.000.07.016.0No0.04.0iqraa0460@gmail.com
1524/6/2023 14:33:02Ashish Khobragade21482ComputerThird year56.079.87https://drive.google.com/open?id=1-awZnTm0zOqjxN_En2NuixDh724gXPUv2.5Educational, Research/ReadingWhatsApp, Instagram, YouTube1.01.000.01.00.0000.00.000.01.015.0No2.02.0ashishkhobragade450@gmail.com
1534/6/2023 14:34:38Siddique Mamoon20461ComputerThird year85.086.00https://drive.google.com/open?id=1rvDQ0LR-EdcyrWEFPQP2iNcnpLi5U9--2.0Educational, Entertainment, Research/Reading, GamingWhatsApp, Instagram, YouTube, Snapchat, Telegram,0.51.000.01.00.2000.00.000.51.012.0Maybe3.02.0mr.five1714111@gmail.com
1544/6/2023 14:38:29Aiman Aijaz Dabir20414ComputerThird year91.090.00https://drive.google.com/open?id=1Ai7a5XjkVtFfWP_vMjHyjTkDMEIHsIxm4.0Educational, Entertainment, Research/ReadingWhatsApp, Instagram, YouTube, Snapchat, Pinterest, Twitter, Telegram,2.02.000.01.01.0001.00.000.01.010.0No2.03.0aimandabir198@gmail.com
1554/6/2023 14:39:04Sayed amaan20440ComputerThird year88.082.00https://drive.google.com/open?id=1LryDk_XSnQzOpPb5fVuYcgV3rO21Zm5612.0Educational, Entertainment, GamingWhatsApp, Instagram, Facebook, YouTube, Snapchat, Discord, Twitter, Telegram6.07.002.05.03.0011.01.003.04.012.0Maybe10.04.0sayedamaan6104@gmail.com
1564/6/2023 14:43:44Khan Omar20426ComputerThird year89.086.00https://drive.google.com/open?id=10qclrJSEJoc6Qx4SeGMT8-2Kmtjvnrlq2.0EducationalWhatsApp, Instagram, YouTube, Snapchat, Pinterest1.00.450.03.01.000.32.00.150.24.016.0No1.04.0khanomar7777777@gmail.com
1574/6/2023 15:01:00Ansari Aatif Salman20507ElectronicsThird year63.869.68https://drive.google.com/open?id=1C37JiZL8xB8dJiyJ-FXPu6Rc_otKogE52.0Entertainment, Research/ReadingWhatsApp, Instagram, YouTube, Snapchat1.01.000.04.00.0000.00.000.05.05.0No4.02.0ansariaatif2004@gmail.com
1584/6/2023 15:25:50Sharif20417ComputerThird year85.085.00https://drive.google.com/open?id=1onoMjyKTyXYFFkeHVPiuo3qk1-DEosda3.5EntertainmentWhatsApp, Instagram, YouTube, Snapchat, Reddit, Discord, Pinterest, Twitter, Telegram2.02.000.01.01.0110.50.500.53.03.0Maybe3.04.0hamdulesharif555@gmail.com
1594/6/2023 15:45:11Naufil20442ComputerThird year90.080.00https://drive.google.com/open?id=1TX8aSVFA_D8QkOAHgzIV4bDcSJkx9VCr2.0Entertainment, Research/ReadingWhatsApp, Instagram, YouTube, Snapchat2.01.000.01.00.5000.00.000.01.516.0Maybe2.00.5naufilsayyed14@gmail.com